In the field of optoelectronics,certain types of data may be difficult to accurately annotate,such as high-resolution optoelectronic imaging or imaging in certain special spectral ranges.Weakly supervised learning can...In the field of optoelectronics,certain types of data may be difficult to accurately annotate,such as high-resolution optoelectronic imaging or imaging in certain special spectral ranges.Weakly supervised learning can provide a more reliable approach in these situations.Current popular approaches mainly adopt the classification-based class activation maps(CAM)as initial pseudo labels to solve the task.展开更多
With the initiation of the National Virtual Simulation Experimental Teaching Project in 2018,educational institutions in China have recognized the significance of virtual simulation technology in reforming traditional...With the initiation of the National Virtual Simulation Experimental Teaching Project in 2018,educational institutions in China have recognized the significance of virtual simulation technology in reforming traditional teaching methods and fostering innovative talent cultivation models.Within the realm of higher education in China,motivating students to sustain their utilization of Virtual Simulation Learning Systems(VSLSs)has become a significant challenge.This article builds upon an assessment of the development status of VSLSs in Chinese higher education and draws upon previous studies to construct a model comprising three dimensions:perceived quality,perceived value,and social influence,with the aim of predicting students’enduring willingness to engage with VSLSs.To achieve this objective,a structural modeling analysis approach is employed to explore the interrelationships among the constructs under investigation,while a survey questionnaire is utilized to collect relevant data.The sample population consists of 274 college students from diverse disciplinary fields in China,including Science,Technology,Engineering,and Mathematics(STEM)and Humanities,Arts,and Social Sciences(HASS).The findings reveal that perceived value significantly influences students’willingness to participate,with perceived benefits exerting a greater impact than perceived costs.Furthermore,the overall quality of the VSLSs,encompassing aspects such as software quality,instructional design quality,and virtual simulation quality,holds substantial influence over students’perceived value.Additionally,societal factors such as course scheduling and recommendations from teachers exhibit a positive impact on students’intention to continue using VSLSs.Building upon these findings,the article presents relevant recommendations aimed at enhancing students’sustained utilization of VSLSs.展开更多
Simulation based structural reliability analysis suffers from a heavy computational burden, as each sample needs to be evaluated on the performance function, where structural analysis is performed. To alleviate the co...Simulation based structural reliability analysis suffers from a heavy computational burden, as each sample needs to be evaluated on the performance function, where structural analysis is performed. To alleviate the computational burden, related research focuses mainly on reduction of samples and application of surrogate model, which substitutes the performance function. However,the reduction of samples is achieved commonly at the expense of loss of robustness, and the construction of surrogate model is computationally expensive. In view of this, this paper presents a robust and efficient method in the same direction. The present method uses radial-based importance sampling (RBIS) to reduce samples without loss of robustness. Importantly, Kriging is fully used to efficiently implement RBIS. It not only serves as a surrogate to classify samples as we all know, but also guides the procedure to determine the optimal radius, with which RBIS would reduce samples to the highest degree. When used as a surrogate, Kriging is established through active learning, where the previously evaluated points to determine the optimal radius are reused. The robustness and efficiency of the present method are validated by five representative examples, where the present method is compared mainly with two fundamental reliability methods based on active learning Kriging.展开更多
文摘In the field of optoelectronics,certain types of data may be difficult to accurately annotate,such as high-resolution optoelectronic imaging or imaging in certain special spectral ranges.Weakly supervised learning can provide a more reliable approach in these situations.Current popular approaches mainly adopt the classification-based class activation maps(CAM)as initial pseudo labels to solve the task.
基金the National Social Sciences Found of China’s Major Program“Research on Virtual Reality Media Narrative”(No.21&ZD326).
文摘With the initiation of the National Virtual Simulation Experimental Teaching Project in 2018,educational institutions in China have recognized the significance of virtual simulation technology in reforming traditional teaching methods and fostering innovative talent cultivation models.Within the realm of higher education in China,motivating students to sustain their utilization of Virtual Simulation Learning Systems(VSLSs)has become a significant challenge.This article builds upon an assessment of the development status of VSLSs in Chinese higher education and draws upon previous studies to construct a model comprising three dimensions:perceived quality,perceived value,and social influence,with the aim of predicting students’enduring willingness to engage with VSLSs.To achieve this objective,a structural modeling analysis approach is employed to explore the interrelationships among the constructs under investigation,while a survey questionnaire is utilized to collect relevant data.The sample population consists of 274 college students from diverse disciplinary fields in China,including Science,Technology,Engineering,and Mathematics(STEM)and Humanities,Arts,and Social Sciences(HASS).The findings reveal that perceived value significantly influences students’willingness to participate,with perceived benefits exerting a greater impact than perceived costs.Furthermore,the overall quality of the VSLSs,encompassing aspects such as software quality,instructional design quality,and virtual simulation quality,holds substantial influence over students’perceived value.Additionally,societal factors such as course scheduling and recommendations from teachers exhibit a positive impact on students’intention to continue using VSLSs.Building upon these findings,the article presents relevant recommendations aimed at enhancing students’sustained utilization of VSLSs.
基金supported by the National Natural Science Foundation of China (Grant No. 11421091)the Fundamental Research Funds for the Central Universities (Grant No. HIT.MKSTISP.2016 09)
文摘Simulation based structural reliability analysis suffers from a heavy computational burden, as each sample needs to be evaluated on the performance function, where structural analysis is performed. To alleviate the computational burden, related research focuses mainly on reduction of samples and application of surrogate model, which substitutes the performance function. However,the reduction of samples is achieved commonly at the expense of loss of robustness, and the construction of surrogate model is computationally expensive. In view of this, this paper presents a robust and efficient method in the same direction. The present method uses radial-based importance sampling (RBIS) to reduce samples without loss of robustness. Importantly, Kriging is fully used to efficiently implement RBIS. It not only serves as a surrogate to classify samples as we all know, but also guides the procedure to determine the optimal radius, with which RBIS would reduce samples to the highest degree. When used as a surrogate, Kriging is established through active learning, where the previously evaluated points to determine the optimal radius are reused. The robustness and efficiency of the present method are validated by five representative examples, where the present method is compared mainly with two fundamental reliability methods based on active learning Kriging.
